Field of the Invention
The present invention relates, in particular, to an object detection apparatus for detecting a main object, an object detection method thereof, and a storage medium.
Description of the Related Art
As a conventional method for detecting a main object in an input image, there is a method discussed in Japanese Patent Application Laid-Open No. 2012-243313, for example. According to the method discussed in Japanese Patent Application Laid-Open No. 2012-243313, an input image is first divided into a plurality of partial regions by using an automatic partition algorithm. In addition, based on a weighted sum of differences in feature amount between a given partial region and the other partial regions among the obtained plurality of partial regions, a salience degree of the given partial region is calculated. Then, a main object in the image is detected based on the obtained salience degree.
In addition, A. Dominik et al., Center-surround Divergence of Feature Statistics for Salient Object Detection, ICCV 2011, for example, discusses another method for detecting a main object in an input image. According to the method discussed in A. Dominik et al., Center-surround Divergence of Feature Statistics for Salient Object Detection, ICCV 2011, a plurality of types of feature amounts is first extracted from an input image, and multiple-resolution images are generated with respect to the feature amounts. In addition, two partial regions of different sizes are set for each of the obtained multiple-resolution images, and a salience degree is calculated based on a difference in statistical distribution (Kullback-Leibler divergence) of extracted feature amount between the aforementioned two partial regions. Furthermore, a salience degree image is generated by integrating the salience degrees obtained for the respective multiple-resolution images, and lastly a main object in the image is detected based on the obtained salience degree image.
T. Kadir et al., An affine invariant salient region detector, ECCV 2004, for example, discusses yet another method for detecting a main object (or a partial region thereof) in an input image. According to the method discussed in T. Kadir et al., An affine invariant salient region detector, ECCV 2004, a plurality of types of feature amounts is first extracted from an input image, and multiple-resolution images are generated with respect to the feature amounts. Then, two partial regions of different sizes are set for each of the generated multiple-resolution images. Thereafter, a salience degree is calculated based on a product of a difference in statistical distribution of extracted feature amount between the aforementioned two partial regions (a distance between scaled probability distributions) and an information amount of the feature amount extracted from one of the aforementioned two partial regions (information entropy). Furthermore, a salience degree image is generated by integrating the salience degrees obtained for the respective multiple-resolution images, and lastly a main object (or a partial region thereof) in the image is detected based on the obtained salience degree image.
As described above, according to the methods discussed in Japanese Patent Application Laid-Open No. 2012-243313 and in A. Dominik et al., Center-surround Divergence of Feature Statistics for Salient Object Detection, ICCV 2011, the salience degree is calculated based on the difference in statistical feature amount distribution in the input image, and the main object in the image is detected based on the obtained salience degree. However, there arises a problem in that the accuracy in detecting the main object degrades if the main object in the image is not visually prominent.
In addition, according to the method discussed in T. Kadir et al., An affine invariant salient region detector, ECCV 2004, the size of the information amount (information entropy) contained in the main object in the input image is calculated, and the main object in the image is detected based on the obtained size of the information amount (information entropy). However, there arises a problem in that this method is susceptible to noise caused by an environmental or observational factor, and the accuracy in detecting the main object degrades in turn.